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This dissertation investigates the application of machine learning to improve decision making in airline operations. In an introductory overview, we1 discuss the airline industry and the challenges of decision making in airline operations: e. g., complex IT infrastructure, interconnected resources, delay management, fuel price volatility and future environmental regulation. Machine learning can efficiently integrate a large volume of data from a variety of data sources and formats to generate accurate predictions. To assess the viability of using machine learning models for decision making in airline operations, we develop a model based on linear regression and gradient boosting to predict aircraft arrival time. Furthermore, we integrate cost index optimization to model the impact of aircraft speed on arrival time. While we find that machine learning can improve prediction accuracy by more than 30 %, the optimal cost index varies according to fuel cost, flight distance and delay costs. We propose an overall reduction in cost index for short-haul flights to reduce fuel cost while maintaining punctual operations. Arrival time predictions serve as input for daily operations planning and control. For network carriers accurate arrival time predictions are key for efficient hub operations. In a next step, we focus on aircraft arrival time prediction for intercontinental flights. We analyze the accuracy of en-route weather data provided by the flight plan, generate features based on en-route weather data and integrate them in our prediction model. We evaluate three machine learning models: linear regression, random forest and gradient boosting. Through our approach, we can assess the impact of en-route weather data. Overall, an increase in prediction accuracy of 25 % is achieved. By including en-route weather data, prediction accuracy is improved by 5 %. Our model outlines the essential features for intercontinental arrival time predictions and assess the value of en-route weather data. Furthermore, we outline organizational challenges in implementing predictive analytics. Future environmental regulations are a challenge for the airline industry. From 2020 onwards net growth in CO2 emissions is prohibited. Thus, airlines need to focus on initiatives to limit fuel consumption. We develop a prediction model for fuel consumption considering ten different aircraft types. Our results show that fuel consumption can be improved by more than 30 % for short and long-haul flights resulting in an annual reduction of 10.5 million e in fuel costs and a reduction on green house gas emissions of 66.000 tons. Furthermore, we assess the pilots’ willingness to integrate our prediction model in their fuel decision. Our analysis shows that high prediction accuracy, model understanding and a long testing phase are essential for acceptance of the prediction model. The main implication is that decision making in airline operations can be substantially improved through machine learning. Therefore, more prediction.
1The term “we” refers to the authors of the respective chapters as denoted at the beginning of each chapter. For the abstract, this refers to the authors of Achenbach and Spinler (2018a,b), Achenbach et al. (2017).
Deal Logic Linde / Praxair
(2018)
Currently in news for the last moment approval from the Federal Trade Commission to make the Praxair and Linde merger a reality, the development of the deal has not been short of hurdles. Initially, Praxair-Linde faced resistance from the European Union under the accusation that the merger would hinder competition. Later, both companies had to undertake multiple selling transactions to meet the anti-trust requirements of the countries in which they operated. Praxair and Linde were finally able to meet the requirements in early November for a successful completion of the merger.
Linde and Praxair, the world’s no. 2 and no. 3 industrial gas suppliers respectively, were in the spotlight due to the size of the merger and the impact that it would have on the gas industry, almost making them a monopoly firm.
Gone with the windfall
(2018)
Due to the debate about the generosity of LTC insurance benefits the German government decided to increase benefits and widen the circle of LTC beneficiaries with the Second LTC Strengthening Act. In this paper, we evaluate the long-term implications of this recent reform for the German LTC insurance scheme. Using the framework of generational accounting we show that the reform has led to a widening of the short-term gap between revenues and expenditure and that the LTC insurance is not sustainably financed, neither pre- nor post-reform. By the early 2020s there will be fiscal pressure for further reforms. From an intergenerational perspective, the reform can be seen as a windfall to current beneficiaries increasing the intergenerational redistribution through the pay-as-you-go system.
Workshop Copyright
(2018)
At the beginning of their career civil servants in Germany can choose between the social health insurance (SHI) system and a private plan combined with a direct reimbursement of the government of up to 70 percent. Most civil servants chose the latter, not only but also because they have to cover all contribution payments in the social system themselves, while normal employees get nearly 50 percent from their employers. The city state of Hamburg decided to change the system by paying a share of the contributions if civil servants choose the social plan. We use a stochastic microsimulation model to analyse which socio-economic types of civil servants could benefit from the Hamburg plan and if this changes the mix of insured persons in the SHI system. Our results show that low income and high morbidity types as well as families have a substantially higher incentive to choose SHI. This reform might thereby increase the adverse selection of high risk cases towards SHI.
The Veblen effect revisited
(2018)
This paper examines the recession probabilities for the Eurozone along four different dimensions: First, we identify the best performing indicators for a recession within the next 12 months based on 43 underlying single variables and their different transformations in a benchmark model. We find that a modified version of the yield curve incorporating the shadow interest rate removes the downward rigidity of the front-leg and restores part of the informational content of the term spread at the zero lower bound. However, the best performing single indicator of the benchmark model is Real M1 followed by the Purchasing Managers Index (PMI), the investment grade corporate bond spread and the Terms of Trade. Second, the paper establishes three submodels to increase the lead-time and the stability of recession models: (i) Monetary transmission channels via principal component analysis; (ii) Bivariate regressions to identify paramount combinations; (iii) Unstable surges vis-à-vis the Hodrick-Prescott trend to detect animal spirits and hawkish mistakes. Third, the analysis is extended over various forecasting horizons (6m, 18m and 24m). Fourth, the results are analyzed from the perspective of risk-affine and risk-averse investors.